ellagic acid, kaempferol, and quercetin from acacia nilotica: … · 2019. 7. 23. · ellagic acid,...

NATURAL PRODUCTS: FROM CHEMISTRY TO PHARMACOLOGY (C HO, SECTION EDITOR)

Ellagic Acid, Kaempferol, and Quercetin from Acacia nilotica:Promising Combined Drug With Multiple Mechanisms of Action

Mosab Yahya Al-Nour1 & Musab Mohamed Ibrahim1& Tilal Elsaman1

Published online: 14 May 2019# Springer Nature Switzerland AG 2019

AbstractThe pharmacological activity ofAcacia nilotica’s phytochemical constituents was confirmedwith evidence-based studies, but thedetermination of exact targets that they bind and the mechanism of action were not done; consequently, we aim to identify theexact targets that are responsible for the pharmacological activity via the computational methods. Furthermore, we aim to predictthe pharmacokinetics (ADME) properties and the safety profile in order to identify the best drug candidates. To achieve thosegoals, various computational methods were used including the ligand-based virtual screening and molecular docking. Moreover,pkCSM and SwissADME web servers were used for the prediction of pharmacokinetics and safety. The total number of theinvestigated compounds and targets was 25 and 61, respectively. According to the results, the pharmacological activity wasattributed to the interaction with essential targets. Ellagic acid, Kaempferol, and Quercetin were the best A. nilotica’s phyto-chemical constituents that contribute to the therapeutic activities, were non-toxic as well as non-carcinogen. The administration ofEllagic acid, Kaempferol, and Quercetin as combined drug via the novel drug delivery systems will be a valuable therapeuticchoice for the treatment of recent diseases attacking the public health including cancer, multidrug-resistant bacterial infections,diabetes mellitus, and chronic inflammatory systemic disease.

Keywords A.nilotica .Ellagicacid .Kaempferol .Quercetin .Multiplemechanismsofaction .ADMETandcomputer-aideddrugdiscovery

Introduction

Acacia nilotica is a tropical and sub-tropical medicinal plantbelonging to the Fabaceae family [1]. No doubt, medicinalplants play a vital role in drug discovery, since they are afflu-ent with bioactive phytochemical constituents that are valu-able in the treatment of various diseases, particularly thosecausing recent threats attacking the public health includingcancer, multidrug-resistant bacterial infections, diabetesmellitus, and chronic inflammatory systemic diseases [2, 3].

The higher incidence of cancer and mortality rate [4], theemergence of bacterial resistance with the declining in the

antibacterial research at several pharmaceutical companies[5], the huge prevalence and complications associated withdiabetes mellitus [6] as well as the long-term suffering asso-ciated with the chronic inflammatory systemic diseases suchas rheumatoid arthritis and multiple sclerosis [7] are leadingforces that encourage us to participate in fighting against theprobable threats. Such an issue is attained via the discoveryand development of efficient innovative anticancer, antibacte-rial, antidiabetic, and anti-inflammatory drugs. Unfortunately,drug discovery is a time-consuming, costive, as well as diffi-cult process [8, 9]; hence, it necessitated to involve sophisti-cated techniques in the drug discovery process in order toovercome those limitations. Recently, one of the promisingsophisticated techniques is the computational tools(computer-aided drug design) that have a valuable impact inthe discovery and development of newer drugs with a reduc-tion in time and cost [8]. They include the ligand-based virtualscreening that is based on the searching for the compoundshaving the highest probability in pharmacological activity[10] and molecular docking that relies on the energy-basedscoring function to identify ligand-target complex lowest

This article is part of the Topical Collection on Natural Products: FromChemistry to Pharmacology

* Mosab Yahya [email protected]; [email protected]

1 Department of Pharmaceutical Chemistry, Faculty of Pharmacy,Omdurman Islamic University, Omdurman, Sudan

Current Pharmacology Reports (2019) 5:255–280https://doi.org/10.1007/s40495-019-00181-w

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http://orcid.org/0000-0002-5044-3948

mailto:[email protected]

mailto:[email protected]

energy [11]. Moreover, they involve the software of pharma-cokinetics, toxicity, and the drug-likeness prediction that workby many algorithms [12] including the graph-based signature[13]. Many studies concerning the application of the compu-tational tools in the discovery of natural-derived drugs wereconducted [14–17].

A. nilotica is opulent of many phytochemical constituentsincluding tannins, alkaloids, terpenoids, and flavonoids.Manystudies were conducted in it resulting in an evidence-basedpharmacological data that revealed the potential pharmacolog-ical activities of the phytochemical compounds including an-ticancer, antibacterial, antidiabetic, anti-inflammatory, andother activity making the plant as a promising source for thedevelopment of innovative, safe, biodegradable drugs withgreat activity. The chemical structure of active A. nilotica’sphytochemical constituents was elucidated, the correlation be-tween the responsible phytochemical constituents for treat-ment and the diseases were conducted [1], but the determina-tion of exact targets that phytochemical constituents bind andthe mechanism of action were not performed; consequently,based on established literature and studies, we aim to identifythe exact targets that phytochemical constituents bind to exertthe pharmacological activity by utilizing the computationalmethods as a tool for the study so as to understand the mech-anism of action. Within the current drug design pipeline, drugtarget identification is a very important step in the understand-ing of the probable mechanism of action, increasing the con-fidence and reducing the attrition in clinical trials [18].Furthermore, we aim to predict the pharmacokinetics(ADME) properties and safety profile with the intention ofidentifying the best drug candidates. The QSAR-based virtualscreening is characterized by great and fast throughput withrespectable hit rank [10]. Molecular docking is valuable topredict the stability of the ligand-target complex that reflectsthe biological activity [19]. The pharmacokinetics, toxicity,and drug-likeness prediction are helpful to identify the bestdrug candidates [12, 20]. To our knowledge, such a study wasnot conducted before.

Materials and Methods

Ligand-Based Virtual Screening

Ligands Preparation

The chemical structure of the reported A. nilotica’s phyto-chemical constituents (25 compounds) [1] was drawn viaMarven Sketch software version 18.5 [21] (Fig. 1). The 3Dstructure was generated in a mol2 format with Open Babelsoftware [22], minimized and optimized with Cresset Flaresoftware [23] at the accurate type calculation method.

Virtual Screening

The screening for the exact target that the phytochemical con-stituents bind was performed via Similarity Ensemble SearchTool [24] and TargetNet web servers [25]. The compoundstructures were submitted in smile format. The targets withhigher probability score were selected for further validationvia molecular docking study (61 targets). The linkage betweenpredicted targets with the diseases was attained via UniProt[26], Pharos [27], and Therapeutic Target Databases [28]. Theresults are listed in Tables 1, 2, 3, 4, 5, and 6.

Molecular Docking

Target Preparation

The 3D structure of selected targets from virtual screeningwas obtained from the RCSB protein data bank [67]. Thestructure with better resolution and validation scores wasselected for the study. In order to validate the dockingresults, multiple 3D X-ray crystallographic structures forthe same target were downloaded in PDB format. For thestructures that have no practically determined 3D struc-ture, Phyre2 [58], SWISS-MODEL web server [49], andRaptorX [59] web servers were used for 3D structuremodeling, then downloaded in PDB format. The targetpreparation was carried out in Cresset Flare software[23] according to the default settings. After preparation,the targets 3D structures were minimized via Cresset Flaresoftware [23] at the normal type calculation method. Thetargets were input to the software in PDB format.

Ligands Preparation

The preparation of reported A. nilotica’s phytochemical con-stituents for molecular docking study was carried out as de-scribed above.

Molecular Docking of Phytochemical Constituents Withthe Predicted Targets

The docking calculations were carried out in Cresset Flaresoftware [23] in normal mode and default settings. Thegrid box was defined according to the co-crystallized li-gands, but in the absence of co-crystallized ligands, thegrid box was defined via picking of active site aminoacids. Beside the A. nilotica’s phytochemical constituents,drugs that are well known to bind with the predicted tar-gets (selected randomly from Therapeutic Target [28] andPharos [27] databases) and the co-crystallized ligandswere used as positive controls. The compounds and thetargets were input in mol2 and PDB format, respectively.

256 Curr Pharmacol Rep (2019) 5:255–280

The results are listed in Tables 1, 2, 3, 4, 5, 6, and 7 andFigs. 1, 2, 3, 4, and 5.

The Pharmacokinetics and Toxicity Prediction

The intestinal absorption, volume of distribution, blood-brain barrier, p-glycoprotein and cytochrome-P enzymesinhibition, the renal OCT2 substrate probability, and totalc learance were predic ted via pkCSM [13] andSwissADME web servers [12]. Moreover, the hepatotox-icity, skin sensitization, the hERG potassium channel in-hibition, AMES toxicity, human maximum tolerateddose, carcinogenicity, oral rate acute, and chronic toxic-ity were predicted via pkCSM web server [13] at thedefault settings via submitting of the chemical structuresin smile format. The results of pharmacokinetics arelisted in Tables 7 and 8 and toxicity in Table 9.

Drug-Likeness and Medicinal Chemistry FriendlinessPrediction

The probability of A. nilotica’s phytochemical constituents tobe as drug candidates was carried via applying of Lipinski,Ghose, Veber, Egan, and Muegge filters. In addition, leadlikeness and synthetic accessibility were used to predict me-dicinal chemistry friendliness. The prediction was carried outvia SwissADME web server via submitting of the chemicalstructures in smile format [12]. The results are listed inTable 10.

Methods Validation

The consistency and the reproducibility of the used tools in-cluding the molecular docking were validated by the resub-mission of the compounds for many times.

Results

The Anticancer Targets

The anticancer activity of A. nilotica’s was attributed to thesuppression of the oncogenic transformations, progression,and development, DNA replication, and transcription.Moreover, the prevention of cancer cells proliferation, inva-sion, angiogenesis as well as the suppression of drug resis-tance and the induction of apoptosis.

The anti-breast cancer activity was due to the inhibi-tion of the aromatase enzyme and estrogen receptor beta.In contrast, the anti-prostate cancer activity is due to thecontrol of metastatic behavior of prostate cancer via theinteraction with nuclear receptor ROR-alpha and the

inhibition of Steroid 17 alpha-hydroxylase (Table 1 andFig. 2).

.

The Antibacterial Targets

The antibacterial activity of A. nilotica was attributed to theprevention of fatty acids, peptidoglycans biosynthesis as wellas the prevention of bacterial resistance to the beta-lactamantibiotics. The fatty acid biosynthesis inhibitory activitywas against different types of bacteria includingMycobacterium, Pseudomonas aeruginosa, and Vibriocholera.

The Antiviral and Antiplasmodial Targets

The antiviral activity was attributed to the action on toll-likereceptor 9. The anti-HIV activity is due to the inhibition ofHIV integrase enzyme. The anti-coronavirus activity is due tocoronavirus replicase polyprotein 1 ab enzyme. Theantiplasmodial activity was attributed to the inhibition of en-zymes MO15-related protein kinase pfmrk and M18 aspartylaminopeptidase as well as the prevention of fatty acid biosyn-thesis via inhibition of the enzymes: β-hydroxy acyl-ACPdehydratase FabZ and hydroxyacyl-[acyl-carrier-Protein]dehydratase (Table 3).

The Antidiabetic Targets

The antidiabetic activity was attributed to the interaction withthe insulin receptor, glycogen phosphorylase enzyme,sodium/glucose co-transporter 2 as well as the aldose reduc-tase enzyme (Table 4).

The Anti-Inflammatory Targets

The anti-inflammatory activity was attributed to the inhibitionof enzymes: arachidonate 15-lipoxygenase, cyclooxygenase-2(COX-2), phospholipase A2, receptor-interacting serine/threonine protein kinase 2, and xanthine dehydrogenase/oxidase as well as the interaction with macrophage migrationinhibitory factor (Table 5).

The Antidiarrheal, Anti-Platelets,and Anticholinesterase Targets

The antidiarrheal activity was attributed to the interaction withthe opioid receptors Mu-type Delta-type. The Anti-plateletsactivity was attributed to the interaction with the P2Y12 re-ceptor. The inhibition of the enzyme acetylcholinesterase iscontributed to the anticholinesterase activity.

Curr Pharmacol Rep (2019) 5:255–280 257

a b

c d

e f

g h

Fig. 1 a–x The 2D chemical structures of the reported A. nilotica’s phytochemical constituents [1]


i j

k l

m n

o p

Fig. 1 (continued)


The Predicted Pharmacokinetic Properties

According to the results, Acacetin, γ-Sitosterol, Kaempferol,Flavone, Lupenone, Lupeol, Niloctane, and Quercetin had thehighest gastrointestinal absorption, tissue distribution (Vd),and respectable total clearance.

Moreover, Flavone, Nilobamate, and Niloctane were perme-able to the blood-brain barrier (BBB). Besides, acanilol-1,

acanilol-2,γ-sitosterol, flavone, lupenone, and lupeolwere foundto be subjected to the metabolism via CYP3A4 enzyme(Table 7).

Moreover, (+)-Mollisacacidin, Catechin, Chalconaringnen-4-O-beta-glucopyranoside, Epicatechin, Niloticane, Kaempferol-7-glucoside, Leucocyanidin, and Nilobamate were free from drug-drug interaction via the inhibition of cytochrome-P (CYP) or P-glycoprotein (P-gp) I and II enzymes (Table 8).

q r

s t

u v

w x

Fig. 1 (continued)


Table 1 Ligand-based virtual screening and molecular docking results regarding the anticancer activity

Target Compounds LBVS Docking score

1- Anaplastic lymphoma kinase enzymeBIt is pivotal in neural cells proliferation

and survival^ [26]

4FNZ* 2XB7*1- Quercetin 1.0 − 8.293 − 8.31a- Staurosporine − − 9.306 10.95• A NZF 1501 – − 7.729 –• A GUI 1501 – – − 10.6

2- Angiopoietin 1 receptorBIt regulates the angiogenesis, adhesion,

cell spreading and the maintenance ofvascular quiescence^ [28]

3L8P* 4X3J*1- Ellagic acid 1.0 − 8.403 − 7.48a- Cabozantinib – − 9.041 − 7.36• A 0CE 2207 – − 13.37 –• A 3WR 1201 – – − 8.56

3- Aromatase enzymeBIt is involved in the estrogen

biosynthesis via conversionof androgens into estrogens^ [26]

3EQM* 5JKW*1- (+)-Mollisacacidin 1.0 − 7.840 − 8.482- Acacetin 0.74 − 8.317 − 8.443- Catechin 1.0 − 8.795 − 8.414- Ellagic acid 1.0 − 8.664 − 8.655- Epicatechin 1.0 − 8.028 − 8.456- Flavone 1.0 − 6.450 − 6.457- γ-Sitosterol 1.0 − 11.51 − 11.48- Kaempferol 1.0 − 7.406 − 7.789- Leucocyanidin 0.99 − 11.77 − 12.110- Naringenin 1.0 − 8.354 − 8.0411- Quercetin 1.0 − 7.579 − 8.42a- Anastrozole – − 7.142 − 8.54• A ASD 601 – − 9.699 –• ATES 601 – – − 9.889

4- ATP binding cassette sub-familyG member

BHas a major role in cancer’s cellsmulti-drug resistance^ [26]

5NJ3* 6FEQ*1- (+)-Catechin-3,5,-gallate 0.95 − 10.01 − 11.172- Acacetin 1.0 − 5.343 − 7.6333- Chalconaringnen-4-O-

beta glucopyranoside0.98 − 7.067 − 10.81

4- Ellagic acid 1.0 − 5.789 − 8.6545- Kaempferol 1.0 − 5.839 − 8.0526- Leucocyanidin 1.0 − 7.817 − 10.377- Naringenin 1.0 − 6.023 − 7.7328- Querecitin 1.0 − 5.979 − 8.520• A NAG 702 – − 5.220 –• A D6T 1002 – – − 14.81

5- Aurora A and B kinase enzymesBAurora A is involved in regulation

of cell cycle progression^BAurora B is a main regulator

of mitosis^ [16]

5ORR* 4B8L*1- Ellagic acid 1.0 − 8.327 − 8.0872- Quercetin 1.0 − 6.627 − 8.572a- Axitinib – – − 6.822• A ADP 401 – − 9.409 –• A A0P 1352 – – − 9.203

6- Bcl-2-related protein A1BPro and anti-apoptotic protein^ [29]

5WHH* 2VM6*1- (+)-Mollisacacidin 0.94 − 7.448 − 7.9252- Catechin 0.95 − 7.922 − 8.9053- Catechinn-7-O-gallate 0.95 − 7.888 − 8.3744- Ellagic acid 1.0 − 8.046 − 7.1305- Epicatechin 0.93 − 7.340 − 8.1506- Kaempferol 1.0 − 7.493 − 7.0617- Leucocyanidin 0.976 − 8.616 − 9.0558- Naringenin 0.981 − 7.774 − 7.3509- Querecitin 1.0 − 8.516 − 8.115a- Venetoclax – − 9.339 − 8.807

7- Caspase 9 enzymeBIs an apoptotic initiator, acting as

an important therapeutic target^ [30]

1JXQ* 2AR9*1- (−)-Epigallocatchin-7-gallate 1.0 − 11.834 − 13.182- Ellagic acid 0.998 − 8.206 − 9.4083- Flavone 0.90 − 6.693 − 7.1734- Gallic acid 1.0 − 6.848 − 7.5625- Kaempferol 0.846 − 8.078 − 8.1586- Quercetin 1.0 − 9.214 − 8.518a- Isatin sulfonamide 34 – − 10.425 − 9.046

8- Cell division control protein 42homology (CDC42)

5UPK* 4YDH*1- Acacetin 0.999 − 8.152 − 8.2702- Acanilol-1 – − 8.348 − 8.431


Table 1 (continued)


BIt participates in the oncogenictransformation,invasion, and tumorigenesis [31]

3- Acanilol-2 – − 8.659 − 8.754• C GNP 200 – − 13.968 –• B GNP 201 – – − 14.66

9- Cell division cycle-7-related protein kinaseBIt is required to initiate the DNA replication^ [32]

4F9B* 4F99*1- Ellagic acid 0.993 − 8.517 − 8.5642- Flavone 0.90 − 6.085 − 6.069• C 0SY 601 – − 12.593 –• A ADP 601 – – − 8.397

10- Cyclin-dependent kinase 1 enzymeBIt is a critical regulator of cell

cycle progression^ [33]

4YC6* Swiss1- Ellagic acid 0.993 − 7.957 − 8.845

11- Cyclin-dependent kinase CDK 4 and 6BCDK4 regulates the cell cycle during G (1)/S

transition^ CDK 6 promotes G(1)/S transition [18]

2W9Z* 4AUA*1- Acacetin 1.0 − 7.428 − 8.2472- Acanilol-1 – − 7.491 − 9.1713- Acanilol-2 – − 7.413 − 9.342a- Fascaplysin – − 6.764 − 9.109• A 4AU 1302 – – − 6.659

12- Cyclin-dependent kinase 9 enzymeBIt is involved in regulation of

Transcription^ [18]

4BCG* 6GZH*1- Acacetin 1.0 − 7.600 − 7.7852- Acanilol-1 – − 8.064 − 8.4353- Acanilol-2 – − 8.852 − 9.146• AT3C 1328 – − 8.724 –• A LCI 2001 – – − 9.039

13- Death-associated protein kinase 1BIt regulates type I Apoptotic, type II

autophagic cell deaths^ [16]

5AUV* 5AUU*1- Quercetin 1.0 − 8.275 − 10.3892- Kaempferol 1.0 − 8.106 − 8.955• A AGI 400 – − 7.349 –• A LU2 400 – – − 10.163

14- DNA topoisomerase 1 enzymeBHas an important function in

DNA replication^ [34]

1SC7* 1TL8*1- 1,6-di-O-galloyl-beta 0.987 − 14.08 − 8.7812- Acacetin 0.67 − 10.42 − 6.7153- Kaempferol-7-gluc 1.0 − 14.05 − 9.436a- Camptothecin – − 12.86 − 7.811b- Edotecarin – − 16.28 − 17.452• C M38 990 – − 10.74 –• D AI3 901 – – − 7.088

15- Ephrin type B receptor 4BIt is important in tumor angiogenesis^ [16]

3ZEW* 6FNI*1- Ellagic acid 1.0 − 9.448 − 9.537• A STU 1889 – − 11.729 –• A DXH 1001 – – − 13.698

16- Estrogen receptor betaBhas a pivotal role in the development

and the progression of tumorsvia the mitogenicaction of estrogens^ [35]

1X7J* 2NV7*1- (+)-Mollisacacidin 1.0 − 9.685 − 9.5072- Acacetin 1.0 − 7.556 − 7.6423- Catechin 1.0 − 9.966 − 9.5514- Catechin-7-O-gallate 1.0 − 9.926 − 9.9785- Dicatechin 1.0 31.247 41.8936- Epicatechin 1.0 − 9.975 − 9.7237- Ellagic acid 1.0 − 9.212 − 8.6468- γ-Sitosterol 1.0 − 1.256 − 2.0889- Kaempferol 1.0 − 8.469 − 9.02710- Leucocyanidin 1.0 − 8.452 − 4.88111- Naringenin 1.0 − 9.140 − 9.36312- Querecitin 1.0 − 8.862 − 9.456• A GEN 201 – − 9.001 –• A 555 501 – – − 9.330

17- Focal adhesion kinase enzymeBIt is essential in angiogenesis, cell

migration and apoptosis^ [16]

4K9Y* 4D58*1- Ellagic acid 1.0 − 8.510 − 9.0522- Quercetin 1.0 − 9.228 − 7.587• A K9Y 701 – − 9.549 –• B BI9 1690 – – − 8.303

18- Glycogen synthase kinase 3 βBIt phosphorylates various proteins i

n the cell cycle and apoptosis.Its inhibitors promote apoptosis^ [36]

4IQ6 5K5N1- Ellagic acid 1.0 − 9.049 − 8.588• B 6QH 401 – – − 6.632• B IQ6 501 – − 8.067 –

19- Inducible nitric oxide synthase 3E7G* 3NQS*


Table 1 (continued)


BIt produces NO that has a tumoricidalaction in macrophage^ [16]

1- (+)-Mollisacacidin 1.0 − 10.097 − 9.9772- Epicatechin 1.0 − 10.205 − 9.7233- Ellagic acid 1.0 − 8.337 − 8.1014- Kaempferol 0.968 − 9.407 − 9.5495- Lupenone 1.0 − 10.186 − 10.5376- Lupeol 1.0 − 12.137 − 10.3547- Naringenin 0.89 − 9.896 − 9.9188- Niloticane 1.0 − 10.340 − 10.4849- γ-Sitosterol 1.0 − 11.662 − 10.76310- Quercetin 1.0 − 9.456 − 9.771a- Curcumin – − 10.024 − 9.775• A AT2 906 – − 8.470 − 8.788

20- Induced myeloid leukemiadifferentiation protein MCL-1

BIt is involved in regulation ofapoptosis and cell survival^ [16]

5UUM* 6B4L*1- Acacetin 0.998 − 8.081 − 8.3862- Ellagic acid 0.999 − 8.125 − 8.2043- Kaempferol 1.0 − 8.208 − 8.6294- Quercetin 1.0 − 8.369 − 8.047a- Obatoclax – − 8.357 − 7.452• A CJY 401 – – − 9.461

21- Serine/threonine-protein kinase pim-1BIt is involved in tumorigenesis,

cell survival, and proliferation^ [16]

6AYD* 6BSK*1- Acacetin 0.994 − 7.992 − 6.8782- Ellagic acid 0.9 − 7.155 − 7.9533- Kaempferol 0.99 − 8.282 − 7.7324- Quercetin 1.0 − 8.875 − 8.342a- Leucettine L41 – − 8.785 − 8.134• A C2J 401 – − 5.852 –• A MVG 405 – – − 8.970

22- Matrix metalloproteinase 9Binvolved in tumor transformation,

progression, survival, angiogenesisand metastasis^ [37]

6ESM* 5CUH*1- Quercetin 1.0 − 11.134 − 9.847• A B9Z 306 – − 8.173 –• A LTQ 306 – – − 11.035

23- M phase inducer phosphataseBIt is a key cell cycle regulator^ [38]

4WH7* 4WH9*1- (+)-Mollisacacidin 0.947 − 9.204 − 8.8352- Catechin 0.947 − 8.297 − 8.1993- Digallic acid 0.982 − 10.300 − 8.9914- Epicatechin 0.947 − 8.049 − 9.1795- Kaempferol 0.994 − 7.152 − 7.5246- γ-Sitosterol 1.0 − 7.169 − 10.4137- Niloticane 1.0 − 7.099 − 7.917• A 8H8 607 – − 4.390 –• A 3M8 601 – – − 6.086

24- Nuclear receptor ROR-alphaBIt is involved in cell growth, differentiation,

and control of metastatic behavior ofandrogen-independent prostate cancer^ [39]

1N83* 3B0W*1- Acacetin 1.0 − 8.342 − 8.0212- Ellagic acid 0.969 − 8.433 − 8.2753- Quercetin 0.857 − 8.974 − 9.179• A CLR 1000 – − 11.358 –• B DGX 1 – – − 15.907

25- Serine/threonine protein kinase Nek2BIt regulates centrosome separation, bipolar

spindle formation in cell mitosis^ [16]

2XNN* 2WQO*1- Quercetin 1.0 − 8.417 − 9.318• A 430 1280 – − 7.282 –• AVGK 1280 – – − 10.033

26- P-glycoprotein 1 and 3BThey involved in multi-drug

resistance^ [40]

4XWK* 2CBZ*1- Acacetin 1.0 − 8.064 − 8.5802- Chalconaringnen-4-O 0.979 − 11.266 − 9.3603- Kaempferol 1.0 − 7.418 − 6.4934- Kaempferol-7-glucoside 0.97 − 11.300 − 9.2215- Querecitin 1.0 − 8.002 − 5.888• A 4C8 1301 – − 8.874 –• A ATP 1873 – – − 9.674

27- Platelet-derived growth factor 1 receptorBIt has a pro-angiogenic action [41]

5GRN* 5K5X*1- Ellagic acid 1.0 − 8.681 − 7.922a- Sunitinib – − 9.363 − 8.234

28- Proto-oncogene tyrosineprotein kinase Src

4MXO* 4MXY*1- Ellagic acid 1.0 − 7.488 − 7.260• B DB8 601 – − 8.930 − 7.870


Table 1 (continued)


BIt participates in cancer cellsinvasion and progression^ [42]

29- Protein kinase C epsilon typeBIt is essential in cell invasion, adhesion,

migration, and regulation of apoptosis^ [16]

1GMI* 2WH0*1- Kaempferol 0.787 − 7.836 − 6.8322- Naringenin 0.99 − 7.543 − 6.772

30- Steroid 17 alpha-hydroxylaseBIt is a key regulatory enzyme,

essential in androgens biosynthesis^ [43]

6CIR* 5UYS*1- (+)-Mollisacacidin 0.83 − 8.744 − 8.4622- Ellagic acid 1.0 − 8.526 − 8.3933- γ-Sitosterol 1.0 − 10.868 − 10.7924- Epicatechin 0.838 − 9.273 − 9.7935- Kaempferol 0.955 − 8.360 − 8.4556- Niloticane 0.998 − 8.485 − 8.7277- Querecitin 1.0 − 8.940 − 8.497a- Galeterone – − 10.681 − 11.190• A 3NQ 601 – − 10.485 –• A 8QD 601 – – − 10.777

31- Tankayrase enzyme 1 and 2BInvolved in cell cycle progression and

telomere homeostasis^ [44]

4U6A* 4HKI*1- Acacetin 0.74 − 10.054 − 11.5962- Flavone 1.0 − 9.475 − 9.961• A 3DN 1402 – − 11.568 –• A FLN 1204 – – − 10.237

32- Telomerase reverse transcriptase enzymeBIt is involved in the regulation of transcription

and has a major role in the activation oftelomerase at cancer^ [45]

5UGW* 5NPT*1- (+)-Mollisacacidin 0.991 − 6.778 − 7.6342- Acacetin 1.0 − 5.023 − 7.1003- Ellagic acid 1.0 − 5.547 − 8.2044- Catechin 0.991 − 6.668 − 7.5905- Epicatechin 0.991 − 6.774 − 7.5706- Kaempferol 1.0 − 5.993 − 7.1287- Leucocyanidin 0.912 − 7.246 − 10.7978- Naringenin 1.0 − 5.848 − 7.5439- Quercetin 1.0 − 5.720 − 7.434a- Berberine – − 22.039 − 9.507• A GSH 1201 – − 7.916 –

33- Transcription factor p65BIt promotes tumor cells proliferation,

suppresses the apoptosis, attracts theangiogenesis, metastasis, remodels thelocal metabolism and energizes theimmune system tofavor tumor growth^ [46]

2RAM* 5 U01*1- (+)-Mollisacacidin 0.987 − 8.153 − 8.5492- (+)-Catechin-5,7 digllate 0.998 − 11.618 − 11.9963- Acacetin 1.0 − 6.599 − 7.4434- Catechin-7-O-gallate 0.883 − 8.719 − 9.4575- Ellagic acid 0.994 − 7.309 − 7.5476- Epicatechin 0.987 − 8.661 − 9.3707- Kaempferol 1.0 − 7.535 − 7.2028- Leucocyanidin 0.986 − 9.687 − 9.9059- Quercetin 1.0 − 7.730 − 8.485a- Bortezomib – − 6.232 − 6.396

34- Tyrosine-protein kinase LynBIt is involved in the control of

proliferation and the inhibition ofapoptosis^ [47]

5XY1* 3A4O*1- Ellagic acid 1.0 − 7.446 − 8.238• A 8H0 601 – − 8.054 –• X STU 902 – – − 10.207

35- Vascular endothelial growth factor receptor 3BIt promotes tumor angiogenesis^ [48]

3WZD* 5EW3*1- Quercetin 1.0 − 8.561 − 8.602a- Axitinib – − 8.699 − 11.087b- Cabozantinib – − 6.576 − 7.936• A LEV 1201 – − 8.648 –• A 5T2 1201 – – − 9.156

In Compounds, the numbers 1, 2, 3, … indicate A. nilotica’s phytochemical constituents, letters a, b, … indicates positive controls, • indicates the co-crystallized ligands, and the italic emphasis indicates compounds with the higher scores. At Ligand-Based Virtual Screening Score (LBVS sco.), en dash(–) means that the compound was not screened. Asterisk (*) indicates the PDB ID. Swiss means that the 3D structure of the target was modeled usingSWISS-MODEL web server [49]


The Predicted Toxicity

According to the results, 1,6-di-O-galloyl-beta-D-glucose,ellagic acid, kaempferol, and quercetin were non-toxic as wellas non-carcinogen (Table 9).

Drug-Likeness, Lead-Likeness, and SyntheticAccessibility Prediction

According to the results, (+)-Mollisacacidin, Acacetin,Catechin, Epicatechin, Kaempferol , Naringenin,Niloctane, and Quercetin were found to be the best leadand drug candidates with good synthetic accessibility,followed by Digallic acid, Ellagic acid, Leucocyanidin,and Melacacidin (Table 10).

Discussion

Despite the enormous conducted studies on the pharmacologyactivity of A. nilotica’s [1], the determination of the target thatcontribute to its activity and the understanding of the mecha-nism of action as well as to assess the pharmacokinetics, safe-ty, and the drug-likeness probability are important issues thatwere not conducted yet. Such studies are required to bring theplant in the drug discovery pipeline so as to design a noveldrug with broad-spectrum of therapeutic activity and safety.

To identify the targets, TargetNet web servers that utilize aQSARmodel based on the chemogenomic data as a predictivealgorithm [25] and Similarity Ensemble Search Tool [24] wereused. To validate the predicted target from the web servers, amolecular docking study was performed using Cresset Flaresoftware [23] that uses the Lead finder program [69] for

Table 2 Ligand-based virtual screening and molecular docking results regarding the antibacterial activity

Target Compounds LBVS Docking Score

1- 3-oxyacyl-[acyl-carrier protein]reductase FabG

BIt catalyzes the first reductive step inthe elongation cycle of fattyacid biosynthesis^ [26]

4BNT* 5OVK* 5END*

1- Acacetin 0.68 − 9.017 − 7.925 − 7.8912- Catechin 0.60 − 10.605 − 9.857 − 8.4873- Epicatechin 0.60 − 10.538 − 8.816 − 8.5184- Kaempferol 1.0 − 9.184 − 8.071 − 7.4975- Quercetin 1.0 − 10.068 − 8.324 − 8.1866- C 36E 1248 – − 5.612 – –

7- D NDP 301 – – − 14.254 –

2- Enoyl-acyl carrier protein reductaseBIt is a limiting step enzyme in

fatty acid biosynthesis, has no homologin mammals^ [50]

4M87* 4NR0* 4O1M*

1- (−)-Epigallocatchin-7-gallate 0.43 − 11.920 − 12.991 − 13.662- (+)-Catechin-4, 5,digallate 0.55 − 13.771 − 13.034 − 14.253- (+)-Catechin-5-gallate 0.6 − 12.496 − 11.710 − 13.054- Quercetin 1.0 − 10.248 − 10.823 − 10.12a- Isonazid – − 5.757 − 5.210 − 5.594b- Triclosan – − 7.254 − 7.110 − 7.585• `A NAD 301 – − 13.553 − 13.298 − 11.81

3- D-alanine D-alanine ligase enzymeBIt is an essential bacterial enzyme in

peptidoglycan biosynthesis^ [51]

6DGI* 5C1P* 3R23*

1- Quercetin 1.0 − 6.267 − 8.968 − 7.719a- Adenosine-5′-diphosphate – − 3.430 − 8.350 − 5.561• A GOL 401 – − 5.112 – –

• D ADP 401 – – − 7.924 –

4- AmpC Beta-lactamase enzymeBIt is responsible for hydrolysis of

beta-lactams, with substrate specificitytoward cephalosporins, has an importantrole in cephalosporins resistance^ [16]

2HDQ* 2PU2* 2R9W*

1- D-pinitol 0.876 − 8.334 − 7.930 − 7.4002- Niloticane 0.992 − 8.691 − 8.713 − 7.2213- Quercetin 1.0 − 9.481 − 9.128 − 8.917a- Clavulanic acid – − 8.808 − 7.732 − 8.108

• A C21 501 – − 6.937 – –

• B DK2 701 – – − 9.133 –

In Compounds, the numbers 1, 2, 3, … indicate A. nilotica’s phytochemical constituents, letters a, b, … indicate positive controls, • indicates the co-crystallized ligands, and the italic emphasis indicates compounds with the higher scores. At Ligand-Based Virtual Screening Score (LBVS sco.), en dash(–) means that the compound was not screened. Asterisk (*) indicates the PDB ID


Table 3 Ligand-based virtual screening and molecular docking results regarding the antiviral and the antiplasmodial activity


1- HIV integrase enzymeBAntiviral target acts on the essential step in

viral replication cycle via catalyzes of viral DNAintegration into host DNA^ [52]

3ZT4* 5KGX*

1- Acacetin 1.0 − 8.771 − 4.5952- Digallic acid 1.0 − 11.163 − 7.2273- Ellagic acid 0.75 − 9.509 − 4.7654- Kampeferol 1.0 − 8.889 − 5.4735- Naringenin 0.76 − 9.118 − 5.3886- Querecitin – − 9.897 − 5.120• A 7SK 301 – – − 4.705• A ZT2 1217 – – − 8.030

2- Corona virus replicase polyprotein 1 abBAntiviral target involved in transcription and

replication of viral RNAs and interacts withhost 40S ribosomal subunit leading to translationinhibition^ [26]

5NH0* 5N5Ov*

1- Quercetin 1.0 − 9.436 − 9.848• A 8X8 301 – − 7.612 –

• A 8O5 401 – – − 10.8873- Toll-like receptor 9BAntiviral and antibacterial target acts as innate

immune receptor acts in recognition ofmicrobial DNA^ [53]

5Y3L* 5ZLN*

1- Acacetin 1.0 − 7.024 − 6.0012- Ellagic acid 0.999 − 7.956 − 6.2253- Epicatechin 0.819 − 8.138 − 6.7964- Kaempferol 1.0 − 7.726 − 6.3695- Leucocyanidin 0.992 − 9.222 − 8.6896- Naringenin 0.889 − 7.664 − 7.2447- Niloctne 1.0 − 7.967 − 6.2128- Quercetin 1.0 − 7.481 − 7.048

4- β-Hydroxy acyl-ACP dehydratase FabZBAntiplasmodial target involved in the fatty

acid biosynthesis^ [54]

3AZB* 3AZA*

1- (+) Mollisacacidin 0.6 − 8.297 − 10.9462- Acacetin 0.67 − 6.787 − 8.4543- Catechin 0.60 − 9.251 − 11.0964- Epicatechin 0.60 − 8.857 − 11.2165- Kaempferol 0.78 − 7.318 − 9.5446- Quercetin 1.0 − 10.906 − 9.286• G KM1 – − 7.913 –

• B KM0 2 – – − 6.6035- Hydroxyacyl-[acyl-carrier-Protein] dehydrataseBAntiplasmodial and antibacterial target responsible

for fatty acid biosynthesis^ [55]

3ED0* 3CF9*

1- Quercetin 1.0 − 10.032 − 9.607• A EMO 163 – − 8.454 –

• A AGI 161 – – − 7.8416- MO15-related protein kinase pfmrk enzymeBAntiplasmodial target that is a cyclin-dependent

kinase enzyme plays a central role in theregulation of cell cycle^ [56]

Raptor x Phyre 2

1- Acacetin 0.74 − 8.125 − 9.482

7- M18 aspartyl aminopeptidase enzymeBAntiplasmodial target involved in host

erythrocyte invasion and the degradationof host hemoglobin^ [57]

4EME* Phyre 2

1- (+) Mollisacacidin 1.0 − 10.319 − 8.8552- Epicatechin 1.0 − 9.281 − 9.8953- Querecitin 1.0 − 10.223 − 9.218

In Compounds, the numbers 1, 2, 3, … indicate A. nilotica’s phytochemical constituents, letters a, b, … indicate positive controls, • indicates the co-crystallized ligands, and the italic emphasis indicates compounds with the higher scores. At Ligand-Based Virtual Screening Score (LBVS sco.), en dash(–) means that the compound was not screened. Asterisk (*) indicates the PDB ID. Phyre2 and Raptor x means the 3D structure of target was modeled byPhyre2 [58] and RaptorX [59] web servers, respectively


docking calculat ion. Moreover, pkCSM [13] andSwissADME web servers [12] were used to predict the phar-macokinetics (ADME: Absorption, Distribution, Metabolism,and Elimination), toxicity, and the drug-likeness probability.

The total predicted targets form the virtual screening withthe highest probability that was validated by the moleculardocking were 61 targets.

The interaction of Acacetin with the cell division controlprotein 42 homolog (CDC42) will prevent the oncogenictransformations. The inhibition of enzymes—anaplastic lym-phoma kinase by Quercetin, cyclin-dependent kinases 1, 4,

and 6 by Ellagic acid and Acacetin, Aurora A and B byEllagic acid and Quercetin, serine/threonine protein kinaseNek2 by Quercetin, proto-oncogene tyrosine-protein KinaseSrc by Ellagic acid, tankyrase 1 and 2 by Acacetin as well asM phase inducer phosphatase by Digallic acid, Epicatechin,and Kaempferol—will prevent the cancer progression anddevelopment.

Moreover, the inhibition of the enzymes—cell divisioncycle-7-related protein kinase by Ellagic acid, serine/threonine-protein kinase pim-1 by Quercetin, Ellagic acid,and Kaempferol as well as DNA topoisomerase 1 by

Table 4 Ligand-based virtual screening and molecular docking results regarding the antidiabetic activity


1- Insulin receptor 2W12* 4OGA*

1- Ellagic acid 1.0 − 9.239 − 8.785a- Ceritinib – − 8.150 − 8.093

2- Glycogen phosphorylase (muscle)BAn important allosteric enzyme in

carbohydrate metabolism^ [16],Bpotential target in type 2diabetes mellitus^ [56]

2ZB2* 3CEJ*

1- Quercetin 1.0 − 11.15 − 9.523• A A46 850 – − 11.44 −• A AVF 833 – − 11.52

3- Sodium/glucose co-transporter 2BThe interaction with this enzyme

inhibits the renal glucosereabsorption, leading to a reductionin plasma glucose level^ [60]

2XQ2* 3DH4*

1- 1,6-di-O-galloyl-beta-D-glucose 1.0 − 12.05 − 12.052- Chalconaringnen-4-O-beta. 1.0 − 10.53 − 10.533- Niloticane 0.961 − 7.634 − 7.8224- Canagliflozin – − 11.11 − 8.030

4- Aldose reductase enzymeBIt is involved in the development

of the secondary diabeticcomplications^ [61]

3RX4* 3V36*

1- (+)-Mollisacacidin 1.0 − 9.074 − 11.202- Acacetin 0.74 − 9.196 − 10.173- Chalconaringnen-4-O-beta-glucose 0.74 − 12.16 − 14.314- Catechin 1.0 − 9.581 − 11.745- Dicatechin 0.959 − 12.70 − 10.046- Ellagic acid 1.0 − 8.425 − 8.8867- Epicatecin 1.0 − 9.513 − 11.738- Kaempferol 1.0 − 8.185 − 10.869- Kaempferol-7-glucoside 0.97 − 12.35 − 14.2710- Leucocyanidin 1.0 − 11.45 − 14.2111- Melacacidin 1.0 − 9.75 − 12.9012- Naringenin 1.0 − 9.618 − 9.86913- Quercetin 1.0 − 8.824 − 10.81a- Epalrestat – − 8.849 − 10.50• A SFI 317 – − 8.393 − 8.61

5- Beta-secretase enzymeBIt is down-regulator of insulin

receptors amounts andsignaling in the liver^ [62]

5MXD* 4BEL*

1- Ellagic acid 1.0 − 7.493 − 7.6772- Quercetin 1.0 − 6.955 − 7.408a- 5,5-Diphenyliminohydantoin – − 4.885 − 5.654• A III 701 – − 5.569 –

• A B3P 1399 – – − 6.797



Table 5 Ligand-based virtual screening and molecular docking results regarding the anti-inflammatory activity


1- Arachidonate 15-lipoxygenase.BHas an important role in theimmune and the inflammatoryresponses^ [16].

4NRE* 2P0M*1- (−)-Epigallocatchin-7-gallate 1.0 − 11.046 − 12.032- (+)-Mollisacacidin 1.0 − 7.845 − 9.5353- Acacetin 1.0 − 7.693 − 9.2834- Catechin 0.984 − 7.864 − 10.055- Dicatechin 1.0 − 9.003 − 11.196- Ellagic acid 1.0 − 6.923 − 8.5257- Epicatechin 1.0 − 8.210 − 9.6628- Kaempferol 1.0 − 7.659 − 9.0589- Leucocyanidin 1.0 − 10.035 − 12.7610- Naringenin 1.0 − 7.857 − 8.76011- Quercetin 1.0 − 7.865 − 9.732a- Diethylcarbamazine – − 7.511 − 7.623• A C8E 702 – − 5.722 –• B RS7 841 – – − 6.830

2- Cycloxygenase-2 enzyme (COX-2).BIt generates the inflammatorymediator’s Prostaglandinsfrom the arachidonic acid^ [62].

5IKQ* 5F1A*1- (+)-Mollisacacidin 0.801 − 10.127 − 8.2462- Acacetin 1.0 − 8.944 − 7.5613- Ellagic acid 1.0 − 7.932 − 7.9694- Kaempferol 1.0 − 9.149 − 7.4445- Leucocyanidin 0.907 − 8.121 − 5.1936- Naringenin 0.991 − 9.426 − 8.3457- Querecitin 1.0 − 9.281 − 7.759a- Diclofenac – − 8.628 − 7.131b- Indomethacin – − 8.771 − 7.162• A JMS 602 – − 8.632 –• A SAL 601 – – − 5.384

3- Macrophage migration inhibitory factor.BIt is a pro-inflammatory cytokinecounteracts the anti-inflammatoryactivity of glucocorticoids^ [16].

5XEJ* 6CB5*1- (−)-Epigallocatchin-5, 7-gallate 1.0 − 8.500 − 11.512- (+)-Catechin-5,7-digallate 1.0 − 10.457 − 12.243- (+)-Mollisacacidin 1.0 − 6.527 − 9.6134- Catechin 1.0 − 6.836 − 9.8555- Dicatechin 0.966 − 7.621 − 8.8436- Epicatechin 1.0 − 7.600 − 9.4117- Kaempferol 1.0 − 5.332 − 8.9278- Leucocyanidin 1.0 − 7.623 − 9.6499- Naringenin 1.0 − 6.853 − 8.88110- Querecitin 1.0 − 5.987 − 9.257a- 3,4-Dihydroxycinnamic acid – − 7.846 − 10.35• A 6UV 204 – − 8.978 –• A EV7 201 – – − 11.79

4- Phospholipase A2 enzyme.BIt is responsible for the release of thearachidonic acid from arachidonylphospholipids, thereby involvedin the initiation of theinflammatory response^ [16].

2B96* 5OW8*1- γ-Sitosterol 0.831 − 8.764 − 7.7982- Digallic acid 0.884 − 8.626 − 8.7883- Kaempferol 0.938 − 8.563 − 7.5564- Quercetin 1.0 − 8.655 − 8.101a- Prostaglandin A2 – − 7.640 − 5.746• A ANN 501 – − 5.760 –

5- Receptor-interacting serine/threonine protein kinase 2.BIt is involved in the formationof the productive inflammatoryResponse^ [63].

6FU5* 6HMX*1- Acacetin 0.962 − 8.185 − 6.1082- Digallic acid 0.924 − 12.137 − 9.1053- Ellagic acid 1.0 − 8.111 − 7.2464- Kaempferol 0.919 − 8.138 − 6.6065- Niloctne 1.0 − 10.128 − 7.0786- Quercetin 0.906 − 8.320 − 6.902• A E7N 400 – − 9.371 –

6- Xanthine dehydrogenase/ oxidaseenzyme. BIt contributes to uric acid formation and generation ofreactive oxygen species^ [16].

2E1Q* 3AM9*1- (+)-Mollisacacidin 1.0 − 11.185 − 11.2602- 1,6-di-O-galloyl-beta-D-glu. 1.0 − 14.648 − 13.0423- Acacetin 1.0 − 9.690 − 9.8474- Catechin 1.0 − 11.260 91.3805- Digallic acid 1.0 − 10.059 − 10.0496- Ellagic acid 1.0 − 9.337 − 11.8307- Epicatechin 1.0 − 11.367 − 11.6698- Kaempferol 1.0 − 9.900 − 10.1849- Leucocyanidin 1.0 − 10.999 − 11.79410- Quercetin 1.0 − 11.177 − 11.371a- Allopurinol – − 6.680 − 6.120• A MTE – − 13.547 − 13.141



Kaempferol-7-glucoside and 1,6-di-O-galloyl-beta-D-glu-cose—will suppress the DNA replication; the inhibition ofenzymes—cyclin-dependent kinase 9 by Acacetin and telo-merase reverse transcriptase by Leucocyanidin, Quercetin,Ellagic acid, and Kaempferol—will suppress the transcription;the inhibition of tyrosine-protein kinase Lyn enzyme byEllagic acid will prevent the cancer cells proliferation; as wellas the inhibition of angiopoietin-1 receptor, proto-oncogenetyrosine-protein kinase Src by Ellagic acid, and protein kinaseC epsilon by kaempferol and Naringnen will suppress thecancer cells invasion.

Furthermore, the inhibition of ephrin type B receptor4 and platelet-derived growth factor 1 receptor byEllagic acid, vascular endothelial growth factor receptor3 by Quercetin, as well as focal adhesion kinase en-zyme Ellagic acid and Quercetin will suppress theangiogenesis.

The inhibition of P-glycoprotein 1, 3 transporters byKaempferol-7-glucoside, Chalconaringnen-4-O-beta-glucopyranoside, Kaempferol, and Quercetin as well as ATPbinding cassette sub-family Gmember 2 by (+)-Catechin-3, 5-digallate, Chalconaringnen-4-O-beta-glucopyranoside,

Table 6 Ligand-based virtual screening and molecular docking results regarding the antidiarrheal, anti-platelets, and anticholinesterase activity


1- Mu-type opioid receptorBAntidiarrheal target, the action

on mu and delta opioid receptorsleads to inhibition of diarrheawithout constipation [64]

4DKL* 5C1M*

1- (+)-Mollisacacidin 0.994 − 7.279 − 9.0512- Catechin 0.994 − 7.822 − 8.7273- Dicatechin 0.983 − 8.880 − 12.2684- Epicatechin 0.994 7.392 − 8.836a- Eluxadoline – − 6.868 − 10.799• A 4VO 401 – – − 11.228

2- Delta-type opioid receptorAntidiarrheal target

4N6H* 4EJ4*

1- (+)-Catechin-3, 5-digallate 0.998 − 11.44 − 10.8082- Dicatechin 0.999 − 12.06 − 11.488a- Eluxadoline – − 7.834 − 7.712• A EJ4 1219 – − 11.01 –

• A EJ4 500 – – − 10.3923- P2Y12 receptorBAnti-platelets target has a

central role in platelet activation^ [65]

4PXZ* 4NTJ*

1- 1, 6-di-O-galloyl-beta-D-glucose 0.756 − 11.99 − 11.3972- Clopidogrel – − 5.521 − 6.873• A 6AD 1201 – − 20.29 –

• A AZJ 1201 – − 19.78 –

4- Acetylcholinesterase enzymeBThe reversible inhibition of

enzyme acetylcholinesteraseincreases the concentration ofacetylcholine that is helpful inneurodegenerative disorders likeAlzheimer’s disease [66]

1H22* 1ODC*

1- (+)-Catechin-4,5-digallate 0.73 − 12.837 − 13.1372- (+)-Mollisacacidin 0.984 − 10.697 − 11.0743- Acacetin 0.74 − 8.728 − 10.1474- Catechin 0.984 − 11.143 − 11.3975- Digallic acid 0.94 − 12.683 − 13.2726- Epicatechin 0.984 − 11.053 − 10.7017- Flavone 1.0 − 7.519 − 7.5218- Leucocyanidin 0.99 − 12.967 − 13.1539- Lupenone 1.0 − 10.976 − 8.96810- Lupeol 0.99 − 11.737 − 10.07111- Melacacidin 0.99 − 11.162 − 11.29112- Niloticane 1.0 − 10.396 − 10.735a- Neostigmine – − 7.919 − 8.196• A E10 1536 – − 11.084 − 11.266• A A8B 1538 – – − 11.266

In Compounds, the numbers 1, 2, 3, … indicate A. nilotica’s phytochemical constituents, letters a, b, … indicates positive controls, • indicates the co-crystallized ligands, and the italic emphasis indicates compounds with the higher scores. At Ligand-Based Virtual Screening Score (LBVS sco.), en dash(–) means that the compound was not screened. Asterisk (*) indicates the PDB ID


Leucocyanidin, Quercetin, Ellagic acid, and Kaempferol willsuppress the cancer cells resistance.

The interaction of Lupeol, Quercetin, Ellagic acid, andKaempferol with the enzyme inducible nitric oxide synthaseon macrophage will promote a tumoricidal action. The inhibi-tion of the enzymes—Bcl-2-related protein A1 byLeucocyanidin, Quercetin, Ellagic acid, and Kaempferol, theinduced myeloid leukemia differentiation protein MCL-1 byAcacetin, Quercetin, Ellagic acid, and Kaempferol as well asthe interact ion with enzymes: caspase 9 by (−)-Epigallocatechin-7-gallate, Quercetin, Ellagic acid, andKaempferol, death-associated protein kinase 1 byKaempferol and Quercetin—will induce cancer cell apoptosis.Consequently, those compounds show substantial anticanceractivity (Table 1).

The interaction of Kaempferol and Quercetin with theenzymes 3-oxyacyl-[acyl carrier protein] reductase FabG

and the interaction of (−)-Epigallocatechin-7-gallate, (+)-Catechin-4, 5-digallate, and Quercetin with enoyl-acyl car-rier protein reductase will inhibit the bacterial fatty acidsbiosynthesis that is essential in the formation of bacterialmembrane phospholipids [70] leading to Ban impairment inthe cellular envelope structure and function, the ability toform biofilms as well as increasing the susceptibility to theenvironmental stress^ [71]. Moreover, the inhibition of theenzyme D-alanine D-alanine ligase by Quercetin will sup-press the peptidoglycans biosynthesis that is vital in bacte-rial cell structure causing loss of bacterial cell integrity[72]. Therefore, those compounds have significant antibac-terial activity (Table 2).

The interaction of Leucocyanidin, Ellagic acid,Kaempferol, and Quercetin with Toll-like receptor 9 will ac-tivate this innate immune receptor that helps in the recognitionof microbial DNA [53]. The interaction of Digallic acid,

Table 7 The predicted pharmacokinetics properties of phytochemical constituents having higher affinity scores (part A)

Phytochemical constituent Intestinalabsorption

BBBpermeability

Human Vd(L/kg)

Total clearance (mg/kg/day)

Renal OCT2substrate

(−)-Epigallocatechin-5,7-gallate Low (14.341%) No 1.29 0.23 No

(−)-Epigallocatechin-7-gallate Low (47.214%) No 1.46 3.4 No

(+)-Catechin-3,5,-digallate Low (44.42%) No 0.97 0.3 No

(+)-Mollisacacidin High (72.264%) No 1.33 1.85 No

1,6-di-O-galloyl-beta-D-glucose Low (28.679%) No 2.65 5.4 No

Acacetin High (94.546%) No 0.78 5.9 No

Acanilol-1 High (98.82%) No 0.34 5.5 No

Acanilol-2 High (96.44%) No 0.3 4.6 No

Catechin High (72.264%) No 1.33 1.9 No

Catechin-7-O-gallate (54.376%) No 1.40 1.0 No

Chalconaringnen-4-O-beta-glucopyranoside Low (17.445%) No 0.51 1.79 No

Dicatechin (69.966%) No 0.75 1.73 No

Digallic acid Low (47.548%) No 1.26 3.5 No

Ellagic acid High (76.935%) No 0.97 4.1 No

Epicatechin High (72.264%) No 1.33 1.85 No

Flavone High (94.935%) Yes 0.96 2.12 No

γ-Sitosterol High (95.884%) No 1.32 4.25 No

Kaempferol High (75.481)% No 1.02 4.52 No

Kaempferol-7-glucoside Low (44.274%) No 0.67 5.17 No

Leucocyanadin (65.231%) No 2.8 1.47 No

Lupenone High (100%) No 1.1 1.27 No

Lupeol High (98.249%) No 0.7 4.19 No

Melacacidin (67.928%) No 4.7 1.01 No

Naringnen High (89.345%) No 0.53 1.33 No

Naringnen-7-O-beta-glucopyranoside (56.167%) No 1.3 1.74 No

Nilobamate High (88.608%) Yes 1.5 21.29 No

Niloctane High (95.567%) Yes 1.1 7.17 Yes

Quercetin High (75.36%) No 2.2 3.83 No

The italic emphasis indicates desirable prosperity

BBB blood-brain barrier, Vd volume of distribution, Renal OCT2 human organic cation transporter 2 [68]


Acacetin, Ellagic acid, Kaempferol, and Quercetin with HIVintegrase enzyme will inhibit the viral DNA integration intohost DNA leading to the suppression of replication cycle [52].The interaction of Quercetin with Coronavirus replicasepolyprotein 1 ab will inhibit the transcription and replicationof viral RNAs [26]. Thus, those A. nilotica’s phytochemicalconstituents exhibit considerable antiviral activity (Table 3).

The interaction of Acacetin with the enzymeMO15-relatedprotein kinase pfmrk will disrupt the regulation of plasmodialcell cycle [56], and the interaction of Quercetin and (+)-Mollisacacidin with the enzymeM18 aspartyl aminopeptidasewill prevent the invasion in host erythrocyte and the degrada-tion of host hemoglobin [57]. Furthermore, the interaction ofQuercetin with the plasmodial enzymes β-hydroxy acyl-ACPdehydratase FabZ and hydroxyacyl-[acyl-carrier-Protein]

dehydratase will inhibit the fatty acid biosynthesis [54, 55]that are important for plasmodial membrane [73].Subsequently, those A. nilotica’s phytochemical constituentsshow considerable antiplasmodial activity (Table 3).

The interaction of Ellagic acid with the insulin receptor willpromote glucose uptake that lowers the blood glucose level[74]. The interaction of Quercetin with the enzyme glycogenphosphorylase will inhibit the glycogenolysis that reduces thehyperglycemia [75]. Moreover, the interaction of 1,6-di-O-galloyl-beta-D-glucose and Chalconaringnen-4-O-beta-glucopyranoside with the sodium/glucose co-transporter 2will inhibit the renal glucose reabsorption leading to a reduc-tion in plasma glucose level [60], the interaction ofDicatechin, Kaempferol-7-glucoside, Leucocyanidin, Ellagicacid, Kaempferol, and Quercetin with the aldose reductase

Fig. 2 The 3D interactionbetween the best compounds withsome of their predicted anticancertargets. a Quercetin (violet) withanaplastic lymphoma kinase en-zyme. Staurosporine (turquoise)as control. b Ellagic acid (darkyellow) with angiopoietin 1 re-ceptor. Cabozantinib (turquoise)as control. c Ellagic acid (darkyellow), kaempferol (pink), andquercetin (violet) with the aroma-tase enzyme. Anastrozole (teal)and the co-crystallized ligand AASD 601(turquoise) as a control.d Ellagic acid (dark yellow) andquercetin (violet) with Aurora Akinase enzyme. The co-crystallized ligand A ADP401(turquoise) as a control. eEllagic acid (dark yellow),kaempferol (pink), and quercetin(violet) with caspase 9 enzyme.The Isatin sulfonamide 34(turquoise) as a control. Ellagicacid (dark yellow), kaempferol(pink), and quercetin (violet) withsteroid 17 alpha-hydroxylase en-zyme. Galeterone (turquoise) as acontrol


enzyme will suppress the development of the secondaryDiabetic complications [61], as well as the interaction ofEllagic acid andQuercetin with the beta-secretase enzymewillupregulate the insulin receptors in the liver [62]. Successively,those A. nilotica’s phytochemical constituents have significantantidiabetic activity (Table 4).

The interaction of (−)-Epigallocatechin-7-gallate, Ellagicacid, Kaempferol, and Quercetin with the enzymearachidonate 15-lipoxygenase and the interaction of Digallicacid, Acacetin, Ellagic acid, Kaempferol, and Quercetin withthe receptor-interacting serine/threonine protein kinase 2 willinterrupt the inflammatory responses [16, 63].

The interaction of (+)-Mollisacacidin, Naringnen,E l l ag i c ac id , Kaempfe ro l , and Querce t in wi thCycloxygenase-2 enzyme (COX-2) will prevent the forma-tion of inflammatory mediators prostaglandins [62], and

the interaction of Digallic acid, Kaempferol, andQuercetin with Phospholipase A2 enzyme will preventthe initiation of the inflammatory response [16].Moreover, the interaction of 1, 6-di-O-galloyl-beta-D-glu-cose, Digallic acid, Acacetin, Ellagic acid, Kaempferol,and Quercetin with the xanthine dehydrogenase/oxidaseenzyme will inhibit the formation of uric acid and reactiveoxygen species [16]. Thus, those of A. nilotica’s phyto-chemical const i tuents exhibi t considerable ant i -inflammatory activity (Table 5).

The interaction of Dicatechin with the mu and delta opioidreceptors will lead to antisecretory and anti-transit action thatwill inhibit diarrhea [76]. The interaction of 1,6-di-O-galloyl-beta-D-glucose with P2Y12 receptor will inhibit the plateletactivation [65]; consequently, they have considerable antidi-arrheal and anti-platelets activity, respectively (Table 6).

Fig. 3 The 3D interactionbetween the best compounds withtheir predicted antibacterial andantiviral targets. a Kaempferol(pink), and quercetin (violet) with3-oxyacyl-[acyl-carrier protein]reductase FabG. The co-crystallized ligand D NDP 301(turquoise) as a control. bQuercetin (violet) with enoyl-acylcarrier protein reductase.Triclosan (turquoise) as a control.c Quercetin (violet) with D-alanine D-alanine ligase enzyme.The co-crystallized ligand D ADP401(turquoise) as a control. dQuercetin (violet) with AmpCbeta-lactamase enzyme.Clavulanic acid (turquoise) as acontrol. e Ellagic acid (dark yel-low), kaempferol (pink), andquercetin (violet) with HIVintegrase enzyme. The co-crystallized ligand A ZT2 1217(turquoise) as a control. fQuercetin (violet) with corona vi-rus replicase polyprotein 1 ab.The co-crystallized ligand A 8X8301 (turquoise) as a control


The chemogenomic-based QSAR models of TargetNetweb server were strictly evaluated and validated leadingto respected screening results [25]. Furthermore, the leadfinder program [69] on Cresset flare software [23] ischaracterized by the combination between the geneticalgorithm and different optimization strategies leadingto great efficiency, robustness, accuracy, and speed ofcalculations [69]. Musab Ibrahim et al. [77] found theresults of a molecular docking study about novel synthe-sized COX enzyme inhibitors conducted in Cresset Flaresoftware were aligned with results of the conductedin vivo study. Depending on that, the obtained resultsof the predicted targets could be with considerableaccuracy.

Since the pharmacological activity does not dependonly on the pharmacodynamic properties, but also on the

pharmacokinetic properties. Moreover, as the drug safety,the assessment of drug-likeness probability, and the syn-thetic accessibility are important issues [78], the identifi-cation of the best A. nilotica’s phytochemical constituentswill be attained by the assessment of those issuescollectively.

The pharmacokinetics is concerning the study of theentrance, movement, changing, and leaving of the drugto the body [79]. The higher absorption from the gastro-intestinal tract leads to higher drug concentration on theblood, the higher volume of distribution provides highersupply to the body tissues, and the adequate metabolismand elimination prevent the accumulation of the drug inthe body, hence reduce the toxicity [79]. Consequently,the consideration of the pharmacokinetics in drug designis an essential task [80].

Fig. 4 The 3D interactionbetween the best compounds withsome of their predictedantiplasmodial and antidiabetictargets. a Kaempferol (pink), andquercetin (violet) with β-hydroxyacyl-ACP dehydratase FabZ. Theco-crystallized ligand B KM0 2(turquoise) as a control. bQuercetin (violet) withhydroxyacyl-[acyl-carrier-Protein] dehydratase. The co-crystallized ligand A EMO 163(turquoise) as a control. c (+)-Mollisacacidin (green), epicate-chin (teal), and quercetin (violet)with M18 aspartyl aminopepti-dase enzyme. d Ellagic acid (darkyellow) with insulin receptor.Ceritinib (turquoise) as a control.e Quercetin (violet) with glyco-gen phosphorylase (muscle). Theco-crystallized ligand AVF 833(turquoise) as a control. f 1,6-di-O-galloyl-beta-D-glucose (green)with sodium /glucose co-transporter 2. Canagliflozin(turquoise) as a control


For instance, Lupenone is highly lipophilic; hence, it hashigher absorption percent (100%); in contrast, the hydro-philic groups of Ellagic acid reduce it absorption percentto 76.935%, however, still it as a high absorption percent.The higher absorption will make Lupenone is highly bio-available. Niloctane is permeable to BBB; therefore, its con-centration that reaches the brain targets is more than Ellagicacid that is not permeable to the BBB. The predicted volumeof distribution (Vd) of Melacacidin (4.7 L/kg) is the highestone, meaning that it has the highest distribution in bodytissues. In contrary, Nilobamate has the highest predictedtotal clearance, meaning that it is the fastest one that elimi-nated from the body (Table 7).

Moreover, the inhibition of cytochrome-P enzymeCYP1A2 by Ellagic acid will decrease the biotransformationof drugs that metabolized by it leading to increase in the con-centration of them that may increase the side effects;

consequently, the drug-drug interaction must be in consider-ation. The binding of Dicatechin with the P-glycoprotein maydecrease the transportation of drugs transported by this trans-porter and may involve in the drug resistance by the pumpingout mechanism (Table 8).

Furthermore, the predicted AMES toxici ty ofEpicatechin will lead to genotoxicity and mutagenicity[81], the predicted hERG II potassium channel inhibitoryeffect of Acacetin Bprolongs the QT interval in ECG thatincreases the risk for potentially fatal ventriculararrhythmias^ [82]; subsequently, such drugs will not beconsidered as drug-likeness candidate (Table 9).

Besides, Quercetin has no violation in Lipinski rule offive; hence, it will a good candidate as an orally activedrug as well as it has no violation in Ghose, Veber, andEgan filters; therefore, it will be a good lead-likeness can-didate [12] (Table 10).

Fig. 5 The 3D interactionbetween the best compounds withsome of their predicted anti-inflammatory, antidiarrheal, andanti-platelets targets and acetylcholinesterase enzyme. a Ellagicacid (dark yellow), kaempferol(pink), and quercetin (violet) witharachidonate 15-lipoxygenase en-zyme. The co-crystallized ligandA C8E 702 (turquoise) as a con-trol. b Ellagic acid (dark yellow),kaempferol (pink), and quercetin(violet) with cyclooxygenase-2enzyme. Indomethacin(turquoise) as a control. cDicatechin with Mu-type opioidreceptor. Eluxadoline (turquoise)as a control. d Dicatechin withDelta-type opioid receptor.Eluxadoline (turquoise) as a con-trol. e 1,6-di-O-galloyl-beta-D-glucose (yellow) with P2Y12 re-ceptor. Clopidogrel (turquoise) asa control. f Digallic acid (yellow)and leucocyanidin (blue) withacetylcholinesterase enzyme.Neostigmine (turquoise) as acontrol


According to the results of pharmacodynamics, phar-macokinetics, safety, and drug-likeness predictions col-lectively, Ellagic acid, Kaempferol, and Quercetin werethe best A. nilotica’s phytochemical constituents thatcontribute to the therapeutic activities. The 3D interac-tion with their predicted targets demonstrates marked li-gand superimposing with the control compounds (e.g.,Figs. 1a, b, 2e, and 5b); however, it may at the sameactive site without ligand superimposing (e.g., Figs. 1cand 2c). Ellagic acid interacts with Aurora A kinase en-zyme with two binding modes (Fig. 1d). Ellagic acid,Kaempferol, and Quercetin interact with Steroid 17alpha-hydroxylase enzyme at a binding mode that differsfrom the binding mode of control Galeterone (Fig. 1f).

They were followed by (+)-Mollisacacidin, Epicatechin,and Melacacidin, those of their predicted AMES toxicity

decreased their rank. The predicted hERG II potassium chan-nel inhibitory effect of Acacetin decreased its rank; however,it has good pharmacodynamics and pharmacokinetics profile.

Despite the efficient pharmacodynamics and the respect-able safety profile of Ellagic acid, Kaempferol, andQuercetin, practically, each compound suffers from the lowbioavailability [83–85], albeit the predicted intestinal absorp-tion of them is high (Table 7). The reduced bioavailability ofEllagic acid is attributed to the poor absorption and rapidelimination from the body [86] (the predicted total clearanceof Ellagic acid is high). The higher topological polar surfacearea (TPSA) (Table 10) contributes to the poor absorption.The reduced absorption of Kaempferol is attributed to thelarger particle size and poor water solubility [83]. The reducedbioavailability of Quercetin is attributed to Bthe poor solubilityand crystalline form at body temperature^ [85].

Table 8 The predicted pharmacokinetics properties of phytochemical constituents having higher affinity scores (part B)

Phytochemical constituent CYP2D6 and CYP3A4substrate

CYP enzymes inhibition P-gpsubstrate

P-gp I or II inhibition

(−)-Epigallocatechin-5, 7-gallate No Non inhibitor Substrate P-gp II

(−)-Epigallocatechin-7-gallate No CYP1A2, CYP3A4 Substrate Non inhibitor

(+)-Catechin-3,5,-digallate No CYP2C9 Substrate P-gp II

(+)-Mollisacacidin No Non inhibitor Substrate Non inhibitor

1,6-di-O-galloyl-beta-D-glucose No Non inhibitor Substrate Non inhibitor

Acacetin No CYP1A2, CYP2C19,CYP2C9,CYP2D6 CYP3A4 inhibitor

Substrate Non inhibitor

Catechin No Non inhibitor Substrate Non inhibitor

Catechin-7-O-gallate No CYP1A2 inhibitor substrate P-gp II

Chalconaringnen-4-O-beta-glucopyranoside No Non inhibitor Substrate Non inhibitor

Dicatechin No Non inhibitor Substrate P-gp I and II

Digallic acid No CYP1A2, CYP3A4 inhibitor Substrate Non inhibitor

Ellagic acid No CYP1A2 inhibitor Substrate Non inhibitor

Epicatechin No Non inhibitor Substrate Non inhibitor

Flavone CYP3A4 CYP1A2, CYP2C19,CYP2C9,and CYP2D6

Substrate P-gp II

γ-Sitosterol CYP3A4 Non inhibitor No P-gp I and II

Kaempferol No CYP1A2 inhibitor Substrate Non inhibitor

Kaempferol-7-glucoside No Non inhibitor Substrate Non inhibitor

Leucocyanadin No Non inhibitor Substrate Non inhibitor

Lupenone CYP3A4 Non inhibitor No P-gp I and II

Lupeol CYP3A4 Non inhibitor No P-gp II

Melacacidin No Non inhibitor Substrate Non inhibitor

Naringenin No CYP1A2, CYP2C19, CYP3A4 Substrate P-gp II

Naringenin-7-O-beta-glucose No Non inhibitor Substrate P-gp II

Nilobamate No Non inhibitor Substrate Non inhibitor

Niloctane No Non inhibitor Substrate Non inhibitor

Quercetin No CYP1A2 inhibitor Substrate Non inhibitor

The italic emphasis indicates desirable prosperity, the bold emphasis indicates undesirable prosperity

CYP cytochrome-P enzyme, P-gp P-glycoprotein transporter


Moreover, Ellagic acid, Kaempferol, and Quercetinhave many polar phenolic hydroxyl groups (structure l,o, and x); consequently, they are subjected to direct glu-curonide conjugation with as phase II metabolism.BKaempferol and Quercetin are rapidly excreted in urineas glucuronides mainly^ [87].

The reduced bioavailability affects pharmacological ac-tivity. Hence, to maintain the pharmacological activity, thebioavailability must be enhanced. The nano-suspensionform of Kaempferol is increased its absorption and bio-availability [83]. The administration of Isoquercetin(Quercetin-3-glucoside) increases the absorption and bio-availability of Quercetin [88]. The bioavailability Ellagicacid, Kaempferol, and Quercetin is increased bypassing theentero-hepatic phase II conjugation (e.g., formation of es-ter derivatives) and by using novel drug delivery systems

as the liposomes. Furthermore, the co-administration ofGinkgo biloba extract with Kaempferol and Quercetin in-creased the bioavailability of them [89]. Consequently, thecombination of Ellagic acid, Kaempferol, and Quercetinwill be optimum treatment choice that maximizes the ther-apeutic activity and the safety profile as well as over-whelms the limits in the bioavailability. As they naturallyare available in one plant, the combination of them at thetherapeutic doses will be additive and will not induce drug-drug interactions. The design of multi-target drug is aneffective promising approach for the treatment of complexdisease [90].

The computational methods including the virtual screeningare not to substitute the in vitro and in vivo methods, however,to reduce the time, cost, and the difficultness in the drug targetidentification [91]. Therefore, this study is an attempt to

Table 9 The predicted toxicity of phytochemical constituents having higher affinity scores

Phytochemical constituent AMEStox.

hERG Ior IIinhibition

Hepatotoxicity Skinsensitization

Carcinogenicity Humanmaximumtolerateddose(mg/kg/day)

Oral ratacutetoxicity(mol/kg)

Oral ratchronic tox.(mg/kg_bw/day)

(−)-Epigallocatechin-5,7-gallate No hERG II No No No 2.73 2.507 6.693

(−)-Epigallocatechin-7-gallate Yes hERG II No No No 4.12 2.728 4.827

(+)-Catechin-3,5,-digallate No hERG II No No No 2.37 2.506 6.19

(+)-Mollisacacidin Yes No No No No 2.57 2.057 1.881

1,6-di-O-galloyl-beta-D-glucose No No No No No 11.83 2.736 4.203

Acacetin No hERG II No No No 5.74 2.558 1.467

Acanilol-1 Yes hERG II Yes No No 1.62 2.52 1.055

Acanilol-2 Yes hERG II No No No 1.65 2.256 1.865

Catechin Yes No No No No 2.57 2.057 1.881

Chalconaringnen-4-O-beta-glucopyranoside Yes No No No No 6.76 2.633 3.95

Dicatechin No hERG II No No No 1.729 2.463 4.729

Digallic acid Yes hERG II No No No 3.86 2.744 4.743

Ellagic acid No No No No No 5.77 2.401 2.013

Epicatechin Yes No No No No 2.57 2.057 1.881

γ-Sitosterol No hERG II No No No 0.46 2.854 1.085

Kaempferol No No No No No 8.13 2.301 2.699

Kaempferol-7-glucoside Yes hERG II No No No 7.64 2.468 4.273

Leucocyanadin Yes No No No No 9.4 2.175 2.974

Lupenone No hERG II No No No 3.48 2.353 1.015

Lupeol No No No No No 0.135 2.712 1.688

Melacacidin Yes No No No No 10.47 2.092 3.379

Naringnen No hERG II No No No 2.36 2.132 1.995

Naringnen-7-O-beta-glucopyranoside Yes No No No No 4.73 2.583 4.019

Niloctane No No No No No 0.27 2.419 1.788

Quercetin No No No No No 11.07 2.221 2.997

The italic emphasis indicates desirable prosperity, the bold emphasis indicates undesirable prosperity

AMES tox. AMES toxicity


Table10

The

predictedlead

likeness,drug

likeness,andsynthetic

accessibility

scoreof

phytochemicalconstituentshaving

higher

affinity

scores

Phytochemical

constituent

Lipinskiruleof

five

aGhose

filtersa

Veber

filtersa

Eganfiltersa

Mueggefiltersa

Leadlik

eness

Synthetic

accessibility

(−)-Epigallo

catchin-5,

7-gallate

3violations

(MW

>500,

rotatb.bonds

>10,H

-don

>5).

3violations

(MW

>480,

MR>130,

atom

s>70)

1violation

(TPS

A>140)

1violation

(TPS

A>131.6)

4violations

(MW

>600,TPS

A>150,H-acc

>10,

H-don

>5)

1violation

(MW

>350)

5.84

(−)-Epigallo

catchin-

7-gallate

2violations

(rotatb.bonds>

10,H

-don

>5)

Noviolations

1violation

(TPS

A>140)

1violation

(TPS

A>131.6)

3violations

(TPS

A>150,

H-acc

>10,

H-don

>5)

1violation

(MW

>350)

4.07

(+)-Catechin-

5,7-digallate

3violations

(MW

>500,rotatb.

bonds>

10,H

-don

>5)

2violations

(MW

>480,

MR>130)

1violation

(TPS

A>140)

1violation

(TPS

A>131.6)

3violations

(TPS

A>150,

H-acc

>10,

H-don

>5)

1violation

(MW

>350)

4.62

(+)-Mollisacacidin

Yes

Yes

Yes

Yes

Yes

Yes

3.50

1,6-di-O

-galloyl-

beta-D

-glucose

1violation

(H-don

>5)

1violation

(WLOGP<−0.4)

1violation

(TPS

A>140)

1violation

(TPS

A>131.6)

2violations

(TPS

A>150,,

H-don

>5)

Yes

4.17

Acacetin

Yes

Yes

Yes

Yes

Yes

Yes

2.98

Catechin

Yes

Yes

Yes

Yes

Yes

Yes

3.50

Catechin-

7-O-gallate

1violation

(H-don

>5)

Yes

1violation

(TPS

A>140)

1violation

(TPS

A>131.6)

2violations

(TPS

A>150,,

H-don

>5)

1violation

(MW

>350)

4.03

Dicatechin

3violations

(MW

>500,

H-acc

>10,

H-don

>5)

2violations

(MW

>480,

MR>130)

1violation

(TPS

A>140)

1violation

(TPS

A>131.6)

3violations

(TPS

A>150,

H-acc

>10,

H-don

>5)

1violation

(MW

>350)

5.32

Digallic

acid

Yes

Yes

1violation

(TPS

A>140)

1violation

(TPS

A>131.6)

2violations

(TPS

A>150,

H-don

>5)

Yes

2.45

Ellagicacid

Yes

Yes

1violation

(TPS

A>140)

1violation

(TPS

A>131.6)

Yes

Yes

3.17

Epicatechin

Yes

Yes

Yes

Yes

Yes

Yes

3.50

γ-Sito

sterol

1violation

(MLOGP>4.15)

3violations

(WLOGP>5.6,

MR>130,

atom

s>70)

Yes

1violation

(WLOGP>5.88)

2violations

(XLOGP3>5,

heteroatom

s<2)

1violation

(MW

>350,

XLOGP

>3.5)

6.30

Kaempferol

Yes

Yes

Yes

Yes

Yes

Yes

3.14

Kaempferol-

7-glucoside

2violations

(H-acc

>10,

H-don

>5)

Yes

1violation

(TPS

A>140)

1violation

(TPS

A>131.6)

3violations

(TPS

A>150,,

H-acc

>10,

H-don

>5).

1violation

(MW

>350)

5.24

Leucocyanadin

Yes

Yes

Yes

Yes

1violation

(H-don

>5).

Yes

3.76


identify the best A. nilotica’s phytochemical constituents thatcontribute to its pharmacological activity as well as their tar-gets. It is not meaning that this study alone will be sufficient tojudge about the result; however, experimental studies are re-quired to validate the results.

Conclusion

According to the results of pharmacodynamics, pharmacoki-netics, safety, and drug-likeness predictions collectively,Ellagic acid, Kaempferol, and Quercetin were the bestA. nilotica’s phytochemical constituents that contribute tothe therapeutic activities; consequently, we recommend theuse of Ellagic acid, Kaempferol, and Quercetin as a combineddrug via the novel drug delivery systems for the treatment ofrecent diseases attacking the public health including cancer,multidrug-resistant bacterial infections, diabetes mellitus, andchronic inflammatory systemic diseases. Moreover, we rec-ommend wet lab studies to validate the results.

Compliance with Ethical Standards

Conflict of Interest The authors declare that having no conflict ofinterest.

Human and Animal Rights and Informed Consent This article does notcontain any studies with human or animal subject performed by any of theauthors

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